While the futuristic notion of a world filled with robotic workers, companions and even enemies is worlds away, pragmatic artificial intelligence (AI) is moving full steam ahead with no plans for stopping. In all of its forms and variations, it’s already starting to deliver real value to businesses.

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Insurers are realizing the benefits AI can bring, automating manual underwriting processes, providing faster and better customer service and predicting risk. Retailers are seeing how AI can help them better understand shopper preferences and consumer behavior. What’s more, health systems are able to predict the likelihood of patient readmissions or how to augment diagnoses and treatment plans.

Major accelerators of AI include the sheer computing power that is available today in the form of graphics processing units (GPUs), the growth of the data scientist profession and increasingly sophisticated algorithms.

Perhaps the biggest factor feeding the swell of AI today is the proliferation of data being produced, stored and shared from Internet of Things (IoT)-based sensors, web pages, databases, social media sites and other tools. It’s interesting that most companies never made a conscious decision to accumulate data, per se — it just sort of happened over time. Yet AI, which unlocks the correlations and mysteries of all that data, requires a much more deliberate pathway to adoption and is often fraught with skepticism.

Kicking The AI Tires

This skepticism may account for the fact that in a McKinsey survey of more than 3,000 companies, worldwide AI adoption outside the tech sector is still at an early, experimental stage. It found that the early adopters tend to be the larger firms deploying AI across their technology groups. They’re adopting AI to increase revenue as well as reduce costs. It also found that among those firms, it has the full support of executive leadership.

Companies that have not adopted AI technology at scale or in a core part of their business are not yet sure of the business case for AI or of the returns they can expect from it. The adoption pattern is showing a widening gap between early adopters and AI laggards. Why are companies so reluctant to join the bandwagon?

Confusion

Companies that have not adopted AI technology are unsure of the business case for AI or of the returns they can expect if they make the costly investment. They don’t understand what specific business problems they may be able to solve using AI.

Wait And See Attitude

In reality, predictive analytics, chatbots and machine learning applications have only taken off in the last few years, led by the large technology companies. Many mid-size firms are waiting to hear about measurable return on investment (ROI) from the early adopters, and for many of them, those metrics may be premature.

Fear Of The Unknown

Companies unfamiliar with AI may envision it as something to be feared, worrying that it will cause harm instead of good. They also fear that it will take away human jobs.

Investment Concerns

Probably one of the biggest reasons companies don’t adopt AI is the sheer cost of it. While partnering with a service provider can substantially offset the costs of deploying AI, companies thinking about initiating an AI project in-house would have to invest in costly GPU capacity, experienced data scientists and of course a complete disruption to their core business processes. Without complete faith in AI, that’s a tough risk to take. […]

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